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Surgical Innovation
August 2018

Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery

Author Affiliations
  • 1Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles
JAMA Surg. 2018;153(8):770-771. doi:10.1001/jamasurg.2018.1512

Mounting research confirms that surgeon performance is directly associated with patient outcomes.1 The current criterion standard for evaluating surgeons is peer review, either during surgery or retrospectively via video footage. Expert review is also used to evaluate performance on robotic surgery. Yet systems data captured directly from the robot provide a novel opportunity to more accurately and objectively measure surgeon performance. A method using data from the robot could increase accuracy and decrease reliance on expert evaluators. We used a novel da Vinci Systems recording device (dVLogger; Intuitive Surgical, Inc) to collect automated performance metrics (APMs) (instrument and endoscopic camera motion tracking and events data, such as energy usage) during live robotic surgery.2 We used machine learning (ML) algorithms—now commonplace outside of medicine—to process these large volumes of automatically collected data (Figure). Machine learning, a form of artificial intelligence, relies on computer algorithms and large volumes of data to “learn” and recognize broad patterns that are often imperceptible to human reviewers. With this process, we can now objectively measure surgeon performance and anticipate patient outcomes; in the near future, we will be able to personalize surgeon training.

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